Hao Liu

This study examines the relationship between sustainable financing and financial risk management of Chinese financial institutions, using data from Chinese banks. Financial risk management is a comprehensive measure of operating performance, asset quality and capital adequacy ratio. The structural vector auto-regression model determines the relationship between two variables. The positive shock of sustainable financing business negatively impacts the financial risk management of banks. In contrast, positive shock of banks’ financial risk management positively affects sustainable financing. Further subdivision of the sample revealed that sustainable financing does not always negatively impact the financial risk management of large state-owned banks. However, the positive shock of financial risk management reduces urban banks’ green credit proportions. The results are consistent whenever compared between the empirical outcome of the entire sample and the sample consisting of national joint stock bank accounts. This comparison helps eliminate the possibility of a biased outcome as a major portion of the sample is from a national joint-stock bank account. Apart from data limitations, the results of the sub-sample test are influenced due to the difference in deposit and loan interest rates, as well as different ownership structures of banks.

The concept of ‘Industry’s Going Upstairs (IGU)’ represents an innovative usage of industrial land that transfers the enterprises’ production to high-rise industrial buildings. It is emerging in the developed areas of eastern China. This study discusses IGU policies to promote local economic development and conducts an empirical test using Guangdong city-level data and a difference-in-differences model. Theoretical analysis shows that IGU can broaden the development space of enterprises and realise industrial and labour agglomeration under supporting policies provided by local governments. The empirical results demonstrate that IGU can improve land-use efficiency and promote local industrial development. IGU is a feasible approach for addressing the current shortage of industrial land in China and is worthy of promotion and replication in other regions.

This paper presents a method of action location in three-dimensional motion teaching. The machine vision technology is used to solve the problems of low positioning accuracy and long positioning time in the traditional motion three-dimensional teaching method. The work of this method is as follows: (a) using machine vision method to determine the world coordinate system of the image; (b) using MRF algorithm to extract the features of 3D teaching action image; (c) determining the spatial correlation of 3D teaching action data. In the three-dimensional teaching action image, the smooth filtering technology is used to suppress and eliminate the noise. Then the convolution neural network (CNN) is used to reconstruct the three-dimensional teaching action image. The entropy of three-dimensional teaching behavior of physical education is determined by CNN. Through a large number of computer simulations, the effectiveness of the proposed system is confirmed. The results show that the system achieves 95% accuracy when the positioning time is 1.9 s.

Research on the endogeneity of directed technical change is very interesting and meaningful. If the direction of technical change is endogenous, policy makers can adjust the technical change value of factors according to specific purpose. We establish a theoretical model of the direction of technical change, relative price of factors and international trade under nested and non-nested CES production functions. We use mature measurement methods such as the unit root test and cointegration analysis to test the theoretical model in practice. We find that the direction of technical change is endogenous in China. The change in the relative price of factors in China causes a technical change in the same direction. Meanwhile, international trade intensifies and accelerates the labour augmenting technical change, but blocks the pace of capital augmenting technical change. Under a substitution elasticity of less than one, technical change is biased toward energy and capital in China, and this bias is brought about by the decrease in their relative price and international trade.

With the continuous increase of mass information in the process of enterprise operation, information redundancy interference poses a challenge to enterprise information decision-making. Therefore, this paper applies big data analysis technology to enterprise information intelligent decision-making, and builds an enterprise information intelligent decision-making model based on big data analysis. The key data of enterprise is mined by using the density weight Canopy to improve the K-Gmedoids algorithm. After sorting, filtering, and transforming, the big data model designed in this paper uses interactive genetic algorithms to obtain the optimal decision-making strategy of enterprise information through experimental tests, which has a significant impact on the decision-making management and the competitiveness of the enterprise. In the process of enterprise information collection and intelligent decision-making, the interactive genetic algorithm generates a 95% match between the optimal business operation plan and the problems that need to be solved in the actual operation of the enterprise, which can better improve the ability of the enterprise to deal with problems. The number of iterations of the optimal decision-making plan obtained by the company is only 6 times, which has a good use effect while improving work efficiency.